Sentiment Analysis using TF-IDF, NLTK, and Logistic Regression

This repository contains a Sentiment Analysis project that utilizes TF-IDF (Term Frequency-Inverse Document Frequency), NLTK (Natural Language Toolkit), and Logistic Regression to analyze sentiment in textual data{tweet). It has been deployed using Streamlit.

Overview

Sentiment Analysis is the process of determining the sentiment or opinion expressed in text data. This project focuses on using machine learning techniques, specifically the TF-IDF vectorization method, NLTK library for natural language processing, and Logistic Regression as a classification algorithm, to perform sentiment analysis on textual data.

Features

• TF-IDF Vectorization: Utilizes TF-IDF to convert text data into numerical vectors, representing the importance of words in a document relative to a collection of documents.
• NLTK (Natural Language Toolkit): Employs NLTK for various natural language processing tasks such as tokenization, stemming, and stop-word removal.
• Logistic Regression: Implements Logistic Regression, a popular classification algorithm, to predict sentiment based on the features extracted using TF-IDF.

Setup and Usage

1. Installation:

   • Clone the repository:

     git clone https://github.com/vishal91-hub/Sentiment-Analysis.git

   • Install dependencies:

     pip install -r requirements.txt

2. Training and Evaluation:

   • Train the model using provided dataset:

     python train.py

   • Evaluate the model:

     python evaluate.py

3. Usage:

   • Use the trained model for sentiment analysis on new text data:

     # Example code snippet
     from sentiment_analyzer import SentimentAnalyzer
     
     sa = SentimentAnalyzer(model_path='path/to/saved/model')
     result = sa.analyze_sentiment("Your text here")
     print(result)

Dependencies

• Python 3.x
• NLTK
• scikit-learn
• Other necessary libraries (specified in requirements.txt)

Contributors

• Vishal Singh

License

This project is licensed under the MIT License - see the LICENSE file for details.